SUMMARY of ROADMAP for BATTERY 2030+ › digitalAssets › 816 › c_816656-l_1-k_3070_d2.… · SUMMARY of ROADMAP for BATTERY 2030+ 2 PREFACE The need to achieve sustainable rechargeable

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2019-06-24

SUMMARY of ROADMAP for

BATTERY 2030+

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PREFACE

The need to achieve sustainable rechargeable batteries with the highest possible energy and

power content, in addition to a long life, is of highest importance to reach the goal of a fossil-

free society.

The list for applications where highly efficient batteries are necessary power and storage units

is long, including electro mobility, large-scale storage, portable electronics, drones, ferries,

medical implants, etc. Environmental sustainability and low carbon footprint of batteries are

among the key properties, but also the amount of energy that can be stored with high

efficiency in a safe way and at a low cost.

BATTERY 2030+ – at the heart of a green connected society – is a large-scale research

initiative, currently supported with a Horizon 2020 Coordination and Support Action (CSA)

for the period 1st of March 2019 to 28

th of February 2020. During this period, a roadmap for

long-term research, including propositions for future research & innovation actions, is to be

formulated, with a full version submitted to the European Commission at the end of the year.

The vision of BATTERY 2030+ is to create the batteries of the future. This means sustainable

batteries with a low lifecycle carbon foot print, batteries with smart functionalities, and a

focus on addressing the obstacles preventing current and future battery technologies to reach

their theoretical performances. BATTERY 2030+ is in this sense “chemistry neutral” to

enable the freedom to do risky and disruptive research. By this long-term focus, BATTERY

2030+ can provide breakthrough technologies to the European battery industry across the full

value chain, enabling long-term European leadership in both existing markets (road transport,

stationary energy storage) and future emerging applications (robotics, aerospace, medical

devices, internet of things).

This summary is the first in a series of drafts with the goal to initiate a strong battery research

movement and create open discussions about the fundamental research needs for Europe. It is

to be used for both written consultations and for discussions and different meetings around

Europe.

We are looking forward to your reactions on this content. Use our webpage

http://battery2030.eu to learn more, and endorse the BATTERY 2030+ initiative to join the

community that will participate in the written consultation on the roadmap (summer 2019)

and in the BATTERY 2030+ workshop (planned for the 20th

of November 2019).

June 2019

Kristina Edström Simon Perraud

Coordinator for BATTERY 2030+ Deputy Coordinator for BATTERY 2030+

Professor at Uppsala University, Sweden CEA, France

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CONTENT

INTRODUCTION

BATTERY 2030+ chemistry-neutral approach

BATTERY 2030+ research areas

Chapter 1: Materials Acceleration Platform (MAP)

Chapter 2: Battery Interface/Interphase Genome (BIG)

Chapter 3: Smart Battery Functionalities

Sensing in BATTERY 2030+: The overall objectives

Self-healing in BATTERY 2030+: The overall objectives

Chapter 4: Manufacturability

Chapter 5: Recyclability

Chapter 6: Other areas to address in a large-scale research program

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INTRODUCTION

In the report “High-energy batteries from 2030+” published in 2017 by Fraunhofer ISI1, it is

stated that “there will be a drastic increase in the global demand for batteries in the next

decade if electric vehicles, portable digital devices and stationary decentralized energy

storage applications continue to take off”. It is also suggested that Europe alone would need a

cell production capacity of at least 200 GWh up to the TWh range, to ensure access to

batteries for European companies. All international institutes making estimates of the future

lithium-based battery market believe in rapid growth the nearest ten years. The European

Union is thus supporting the establishment of a competitive battery value chain in Europe, as

explained in the report published in April 2019 by the European Commission, to provide a

state of play of the main actions undertaken in the framework of the Strategic Action Plan on

Batteries2. This publication is a part of the fourth Report on the State of the Energy Union

3.

Even if the current generation of lithium-ion batteries (as the most powerful of the

rechargeable batteries on the market today) will be the dominating form of high-capacity

rechargeable batteries in the nearest future, there is a need for sustainable and affordable

batteries that have higher energy and/or power content, longer lifetime and a wider

temperature operation range. Preferably all these parameters should be fulfilled in the same

battery cell simultaneously, and it must meet high safety standards. This is a highly

challenging task. To achieve this, new disruptive ideas are needed that can enable the

discovery of the batteries of the future. There is also an urgent need to develop better tools

that can accelerate the discovery and development of new materials and cells, to support new

manufacturing methods and simpler effective recycling measures. Due to this, a long-term

and large-scale research initiative, such as BATTERY 2030+, aims to support Europe to

coordinate the battery research community in the different member states to collaborate on

unified goals, and to reduce the risk for fragmentation. BATTERY 2030+ aims to leapfrog the

battery research field, by looking beyond the current roadmaps that mostly target the nearest

10 years, such as, for example, the European SET-Plan Action 74, and other international

roadmaps. The BATTERY 2030+ roadmap will suggest a number of critical tools and actions

to take within the next 10 years, to provide breakthrough technologies to the European battery

industry across the full value chain, enabling long-term European leadership in both existing

markets (road transport, stationary energy storage) and future emerging applications (robotics,

aerospace, medical devices, internet of things, etc.).

This summary presents some of the content of a long-term roadmap, with the vision to invent

the batteries of the future. It describes the most important opportunities and obstacles to

address in order to develop batteries with higher energy and/or power content, longer life

preventing ageing phenomena, which will be safe, sustainable and possible to manufacture

1 EnErgiEspEichEr-roadmap (UpdatE 2017) HocHenergie-Batterien 2030+ und PersPektiven zukünftiger

BatterietecHnologien, Fruenhofer ISI 2017.

https://www.isi.fraunhofer.de/content/dam/isi/dokumente/cct/lib/Energiespeicher-Roadmap-Dezember-2017.pdf 2https://ec.europa.eu/commission/sites/beta-political/files/report-building-strategic-battery-value-chain-

april2019_en.pdf 3 https://ec.europa.eu/commission/publications/4th-state-energy-union_en 4 SET-Plan action 7. https://setis.ec.europa.eu/sites/default/files/set_plan_batteries_implementation_plan.pdf

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and recycle with low carbon footprint. A detailed roadmap will be presented at the beginning

of 2020, along with a description of the state-of-the-art..

BATTERY 2030+ chemistry-neutral approach

The BATTERY 2030+ roadmap defines both the obstacles to be overcome and the tools to

develop in order to reach ultra-high performance, reliable, safe, sustainable and affordable

batteries. The research areas proposed in the BATTERY 2030+ roadmap (see next paragraph)

are chemistry neutral, which means that they can potentially be applied to any battery

chemistry, creating an impact on both state-of-the-art and future electrochemical storage

systems. In other words, the generic approaches developed by BATTERY 2030+ will have

the potential to boost performances, reliability, safety and sustainability not only in current

and future lithium battery chemistries, but also in post-lithium battery chemistries (sodium-

ion, multivalent metal-ion, metal-air, etc.) as well as in future battery chemistries (see figure 1

below). In particular, BATTERY 2030+ will provide solutions to reduce the gap between

accessible and theoretical performances of those different battery chemistries.

Figure 1. BATTERY 2030+ chemistry-neutral approach will have an impact on both state-of-the-art and future

electrochemical storage systems.

BATTERY 2030+ research areas

Battery research occurs across the full value chain, can be oriented towards battery cells, and

can be based on competences in chemistry, physics, materials science, modelling,

characterisation etc. It can, however, also be oriented towards systems where the battery cells

are integrated to packs, to be used in different applications. Here, the field relies on

knowledge about electronics, electrical engineering, systems-control, modelling at system

level, artificial intelligence (AI) and machine learning – just to mention some. Also, research

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in recycling has become more important and again rely on chemistry, metallurgy, physics and

materials science linked to the use of new efficient characterisation tools.

The areas of research advocated by BATTERY 2030+ rely on these cross- and

multidisciplinary approaches with a strong wish to integrate also other areas of research to

enable cross fertilization. This strategy will be presented in the following chapters.

Chapter 1 describes the need to accelerate the exploration of new battery materials,

electrolytes and interfaces/interphases, by implementation of a Materials Acceleration

Platform (MAP)5. The MAP will enable closed-loop materials discovery and development

through the use of AI to orchestrate data acquisition and analysis from multi-scale computer

simulations, experiments and testing. This also includes the development of autonomous high

throughput synthesis robotics and experiments utilizing the Europe large-scale synchrotron

and neutron facilities.

Chapter 2 focuses on interfaces in batteries which are arguably the least understood part of

the battery, despite the fact that most of the critical battery reactions occurs here, e.g. the

formation of dendrites, the solid electrolyte interphase (SEI) and the cathode interface (CEI).

Accelerated design of battery materials requires a detailed understanding of the fundamental

“Battery Interface/Interphase Genome” (BIG), but to date interfaces/interphases have mainly

have been studied “post mortem” and with ex situ techniques. By combining generative deep

learning, multi-scale modelling and high throughput experiments studying interfaces in situ

and operando as a battery cell is operating we can take a new step towards better and more

reliable batteries of the future.

By integrating the actions taken in Chapter 1 and 2, the aim is to establish predictive BIG

models depicting spatio-temporal evolution of the interfaces which can be integrated into the

MAP, establishing “BIG-MAP”.

Chapter 3 is devoted to smart battery functionalities. It is based on new simpler but still

advanced sensors, which can be inserted directly into battery cells and which can show

dysfunctional components with spatial and time resolution, and thus probe reactions occurring

at interfaces. It also describes a relatively new and unexplored area for batteries – that of self-

healing. Inspired by the field of drug delivery and of the paint industry, self-healing binders

and electrolyte components can be made to increase the reliability of a battery cell.

Chapter 4 and 5 describe cross-cutting research areas. All new disruptive ideas tested with

BIG-MAP, sensing and self-healing chemistries must be able to be upscaled, manufactured

and recycled simply and affordably. Chapter 4 describes the steps to ensure manufacturability

and likewise Chapter 5 describes the steps to ensure recyclability.

Chapter 6 discusses the need to enlarge this large-scale research initiative with new areas of

studies again being “chemistry neutral”, which in principle means that new ideas can be tested

on a suitable battery chemistry to addresses the different challenges that are needed to be

tackled in order to move towards ultra-high performances.

5http://mission-innovation.net/wp-content/uploads/2018/01/Mission-Innovation-IC6-Report-Materials-

Acceleration-Platform-Jan-2018.pdf

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Chapter 1. Materials Acceleration Platform (MAP)

The long term vision in BATTERY 2030+ is to develop a versatile and chemistry neutral

materials discovery platform, capable of achieving a 10-fold increase in the rate of discovery

of novel battery materials, electrolytes and interfaces/interphases. This platform will help to

establish a detailed fundamental understanding of the complex and limiting reactions taking

place within a battery cell, and to discover and design new materials with improved stability

and energy storage properties. To achieve the 10x acceleration, it is also important to

understand the process of how battery electrode and electrolyte materials are formed and

evolve, in order to design their ultimate properties. The backbone of this vision is the

development of the “Battery Interface Genome - Materials Acceleration Platform (BIG-

MAP)” discussed in Chapter 1 and 2, which will enable “inverse design” of ultra-high

performance battery materials and interfaces/interphases. Ultimately, the MAP and the

underlying data-infrastructure described in this chapter, will be capable of integrating cross-

cutting aspects like manufacturability (Chapter 4) and recyclability (Chapter 5) directly into

the discovery process, e.g. by introducing a bias towards materials compositions and

structures that favour e.g. in situ recycling6.

The battery MAP will provide a quantum leap over existing approaches in computer assisted

materials design, e.g. the American Materials Genome Initiative7, by directly integrating

cross-sectoral disciplines ranging from machine learning, over atomic and multi-scale

simulation, to big data analytics and autonomous synthesis robotics with feedback from in-

line characterization and testing. The discovery process will be orchestrated by a central AI

that controls the data acquisition and analysis from all parts of the MAP (Figure 2). The

central AI will be capable of initiating new synthesis procedures, dictating modifications of

experimental conditions for (ongoing) characterization and testing experiments, or launching

a series of new computer simulations based on the available data at any given time.8 Through

active learning, the MAP will progressively improve the quality of the predictions as the

training set grows.

6 DOI: 10.1039/c8cs00297e

7 de Pablo, J. J. et al., Curr. Opin. Solid State Mater. Sci. 18, 99–117 (2014); doi: 10.1038/s41524-019-0173-4)

8 DOI: 10.1016/j.ensm.2019.06.011

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Figure 2. Illustration of the Battery Interface Genome – Materials Acceleration Platform (BIG-MAP)

infrastructure.

The fully developed MAP will also utilize direct integration of the novel concepts developed

in the area of sensors and self-healing, which will be discussed in Chapter 3. To illustrate the

concept, one can imagine having temperature, strain and/or pressure sensors located at an

electrode-electrolyte interface. The input from these sensors is fed directly into the MAP,

where a generative deep learning model provides a prediction of the spatio-temporal evolution

of the materials properties and the interface dynamics. If the model suggests, e.g., increased

gas evolution, this information can be sent directly to the Battery Management System

(BMS), which can then initiate a pre-emptive release of a self-healing agent by application of

external stimuli (see Chapter 3 for details).

The short, medium and long-term goals and objective of the MAP are presented jointly with

those of BIG presented in the following chapter.

Chapter 2. Battery Interface/Interphase Genome (BIG)

Battery interfaces/interphases constitute a combined blessing and a major limitation for nearly

all battery chemistries and cell designs. Interfaces/interphases are of utmost importance for

the practical operation of the battery as well as detrimental processes like aging. To

understand and control their functions is key for the fast development of novel ultra-

performing batteries.

The traditional and slow paradigm of trial-and-error based battery R&D starts from a known

battery materials composition and structure, and subsequently relies on human intuition to

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guide the optimization to improve the performance. Put simply, BATTERY 2030+ intends to

reinvent the way we invent batteries and enable inverse design of battery materials,

electrolytes and interfaces/interphases. This will effectively invert the conventional discovery

process by allowing the desired performance goals to determine the optimal initial materials

composition/structure and applied external stimuli, which will ultimately lead to

interfaces/interphases with the target properties, without a priori defining the starting

composition or structure of the interface.

In order to develop and implement predictive AI-based models for inverse design of battery

interfaces and interphases, it is essential to incorporate the relevant physical understanding,

i.e. the battery interface genome, into the models. BIG aims to establish the fundamental

“genomic” knowledge of the evolution of battery materials, electrolytes and

interfaces/interphases through time and space. However, while the genome in nature is

transferable between species, allowing the sequencing of genome as a whole, this is not

necessarily the case for materials or molecules. Nevertheless, when focusing on

interfaces/interphases, BIG can ensure the transfer of critical information across different

chemistries, structures and materials by developing a platform to measure, understand and

rationalize the different processes occurring from the atomic / molecular to the materials /

battery scale.

BIG will provide the fundamental basis needed for the identification of suitable

“interface/interphase descriptors” or genes coding for the formation and spatio-temporal

evolution of the interfaces/interphases. This understanding is a prerequisite for reliable multi-

scale design of interfaces/interphases, which simply cannot be established within existing

methodologies. Data-driven approaches like semi-supervised feature/representation learning

may hold the key, if the specific domains are bound by the relevant physical constraints8.

Achieving the ambitious BIG-goals requires a concerted effort by the whole European battery

community, targeting focused development of complementary capabilities for multi-scale

modelling and systematic multi-technique experimental characterization of battery

interfaces/interphases. This will include the use of standardized testing protocols and data-

formats for simulations and high throughput experiments and testing, as well as high fidelity

operando characterization at large scale research facilities, to generate/collect comprehensive

sets of high fidelity data that will feed into the common MAP data-infrastructure described in

Chapter 1. Combining different modelling and characterization techniques depending on their

sensitivity in space and time is crucial to design an integrated strategy to fully characterize the

battery interphases formation and evolution (Figure 3).

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Figure 3. Schematic illustration of the applicability of techniques as the complexity of the interphase increases

with evolution (adapted from [8]).

BIG will enable predictive simulations based on the generated insight in the expected spatially

resolved time evolution of the system. The described approach relies on the concept that the

information on the spatio-temporal evolution of complex electrochemical systems is encoded

in physical theories, which have been developed over many decades. The development of

novel complementary and transferable computational and experimental multi-technique

approaches targeting increased spatial resolution, time domains and operando conditions is

needed to give new insights into the construction of ultra-performing battery systems.

BIG will play a key role in the development of models capable of performing an inverse

mapping from the desired properties to the original composition of the materials and external

parameters/conditions8. These physics-aware deep learning models represent a disruptive and

highly efficient way to optimize the utilization of all available data and build the required

bridges between different domains and sectors in the battery discovery cycle. This will help to

resolve the biggest challenges for battery interphases in emerging chemistries, rather than

empirical development of battery chemistry and assembly, which has been the norm so far.

Figure 4 outlines a possible workflow for establishing BIG, which would enable a general

classification of phenomena and categorization of (model-type) interfaces/interphases, which

is currently not available. This would help to establish and extract common features (interface

descriptors) and deviations to the main genome regarding the chemical, structural evolution of

solid/liquid, gas/solid and solid/solid interfaces/interphases, the dynamic restructuring of

complex electrodes.

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Figure 4. Work-flow for the creation of the Battery Interface Genome (BIG).

Full integration of the Battery Interface Genome into the described battery Materials

Acceleration Platform (BIG-MAP) will occur stepwise according to the following the

combined timeline for Chapters 1 and 2:

In the short term: Establish formats and standards of a shared BIG-MAP data infrastructure

for closed loop materials discovery; Autonomous analysis modules for experiments and

simulations results using AI; Computational workflows to identify and pass features between

scales; Data-driven materials and interface models guided by physical understanding.

In the medium term: Implementation of the autonomous BIG-MAP platform capable of

integrating computational modelling, autonomous synthesis robotics and materials

characterization; Demonstrate inverse design of battery materials and interphases; Integration

of sensing and self-healing in BIG-MAP.

In the long term: Fully autonomous and chemistry neutral BIG-MAP platform establish and

demonstrated; Integration of battery cell assembly and device-level testing; Inclusion of

manufacturability and recyclability in the materials discovery process; Digital twin for ultra-

high throughput testing on cell level implemented and validated.

Chapter 3. Smart Battery Functionalities

The disruptive vision of BATTERY 2030+ is to inject smart sensing and self-healing

functionalities into a battery cell with the goal to increase the battery durability, enhance its

lifetime by extending the reliable operation, lower its cost per kWh stored, and finally

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significantly reduce its environmental footprint. Non-invasive sensing technologies with

spatial and time resolution will be developed to monitor battery cell key parameters when it is

in operation and to determine defective spots or components within the cells that need to be

repaired by injection/addition of self-healing functions. Sensing, being upstream of self-

repair, will be presented next with its own goals for development, and then the suggested

vision for self-healing will be described.

Sensing in BATTERY 2030+: The overall objectives

With high sensitivity and resolution measures, sensors that can monitor multiple parameter

changes – such as chemical composition, strain, temperature and pressure at various

locations/components within the cell (spatially resolved monitoring) – are necessary and need

to be discovered and developed. Injecting smart functionalities into the battery cell will mean

the integration and development of various different sensing technologies, which rely on

either optical, electric, thermal, acoustic or even electrochemical concepts to transmit

information in/out of the cells. BATTERY 2030+ will specially emphasis the development of

sensors that can measure multiple parameters with great accuracies, and enable the ultimate

access to the dynamics of the solid electrolyte interphase (SEI).

For successful implementation of the sensing tool into a practical battery, sensors will have to

be adapted to the targeted cell environment in terms of (electro)chemical stability, size and

manufacturing constraints. Sensors with innovative chemical coatings and extremely high

(electro)chemical-thermal stability are needed. Equally, injection of sensors into the battery

cell will necessitate a reduction of their size to a few microns (electrode-electrolyte thickness)

without affecting the cell performance. Manufacturing-wise, a pressing goal is to make

sensors an integral part of the battery cell with the ultimate challenge being the development

of wireless sensing. The purpose of this is to bypass today’s cumbersome connectivity

issues,which limits the role of the sensors. Hence, the importance of conjointly developing

innovative new cell assembly concepts in collaboration with the manufacturing area (Chapter

4).

Additionally, to ensure societal impact, we will follow an holistic approach considering

battery pack design, battery management system (BMS) and applications. Sensing will

provide a colossal amount of data that is a blessing for machine learning and artificial

intelligence (AI). Wisely providing the BMS with the data obtained by smart sensing

functionalities is another indispensable aspect to consider. Obviously, this part will greatly

benefit from the AI and transversal efforts that are being planned within BATTERY 2030+, to

encourage the development of sophisticated BMS and thermal battery management system

(TBMS) systems, benefiting from the synergy between AI and sensing.

This will be made sequentially.

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In short term: Develop, at the battery cell level, non-invasive multi-sensing approaches

relying on various sensing technologies and simple integration. Monitoring the normal-

abnormal evolution of the battery key parameters during cell operation and defining the

proper transfer functions from sensing to BMS.

In medium term: Miniaturisation and integration of the (electro)chemically stable identified

sensing technology(ies) with multifunction at the cell level but also in real battery modules, in

a cost-effective way, compatible with industrial manufacturing processes. Establishment of

new self-adapting and predictive controlled algorithms exploiting sensing data for advanced

BMS.

In long term: Master the communication of the sensors with an advanced BMS relying on the

new AI protocols by wireless means to achieve a fully operational smart battery pack.

Self-healing in BATTERY 2030+: The overall objectives

Sensing is the first step towards battery reliability, which ultimately calls for the injection of

self-healing functions within the battery. A disruptive auto-repairing approach will be pursued

with guidance from the medical field, which heavily relies in the vectorization of drugs for the

treatment of diseases. Efforts will be devoted to the development of a self-healing tooling

within the cell. Procedures like on-demand administration of molecules that can solubilize a

resistive deposit (SEI), or self-repairing polymers containing micelles that encapsulate healing

agents to restore a faulty electrode within the cell are some examples of self-healing

functionalities. Supramolecular architectures which could be physically or chemically cross-

linked, separators capable of accommodating specifically designed molecules/polymers and

release them on demand by physical and chemical stimuli to repair the "tissue" that

constitutes the electrode / electrolyte interface are possible options. Equally, of paramount

importance is the development of a bio sourced membrane with controlled functionalities and

porosity for ion detection, trapping or regulation.

This will be made sequentially:

In short term: Reunite excellent European research teams at the cross-over of various

disciplines to launch the foundation of a new research community for developing self-healing

functions for batteries. Engineer functionalized separators and develop supramolecular

assemblies relying on H-H bonding for reversible crosslinking

In medium term: Wisely engineer separators with micelles holding organic-inorganic

healing agents with various functionalities that can be triggered via a magnetic, thermal or

electric modulus for auto-repairing while being electrochemically transparent. Determine

response-time associated to stimulus actuated self-healing actions for repairing failures

pertaining to electrode fracturing or SEI coarsening.

In long term: Design and manufacture low cost bio sourced membranes with controlled

functionalities and porosity for ion detection and regulation mimicking channels made by

proteins from life science.

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Overall, the integration of novel sensing

functionalities at the cell level with an

efficient BMS having a real active

connection to the self-healing function, as

pictured in the attached Figure 5, is our

long-term objective. Our envisioned

2030+ battery, will not any longer be a

black box, but will have, in addition to the

classical + and – poles, an analytical

output to transmit signals to the BMS,

which could send back a stimulus to

trigger self-healing functions, hence

improving the battery life and its

predictability.

Chapter 4. Manufacturability

The development of new materials with different properties and processing needs such as

those coming out of the BIG-MAP approach (as described in Chapters 1-2) and requirements

along with the integration of sensors and mechanisms for self-healing (as described in Chapter

3) will require a significant rethink of cell design and manufacturing. The redesign of cell

architecture in such a way as to ensure cost effective, minimal energy demand, minimal waste

manufacturing and best use of resources is essential to drive both competitiveness and

sustainability and will play a central part in the BATTERY 2030+ initiative. These challenges

are also a driving force for innovation in the area of conventional battery manufacturing.

On the other hand, during the development process of novel high-performance cells, a

remarkable number of experiments are needed to formulate electrode materials, determine the

cell architecture, scale up the prototype and finally test it. These experiments are time

consuming and expensive.

Usually the manufacturing research is led by the internal knowhow and expertise of sicientists

and technnicians at either industrial, research or academic labs. The adjustment of process

parameters in production lines requires expensive and time-consuming characterisation

experiments. In order to overcome the current paradigm, minimising trial and error, and

facilitating the take up of innovative, breakthrough battery technologies, a full understanding

of the behaviour of the different cell components and complex phenomena is required.

Broadly speaking, any new battery technology or concept will need to face at least two main

validation phases. First, they will need to prove their potential at some prototype level;

second, the feasibility of their upscaling into industrial level will need to be assessed. The

approach that is described below will be useful at both levels: prototype and industrial

manufacturing. Cell design, understood as a necessary step between innovative materials and

the actual battery technology derived from them, and that which will ultimately be needed to

manufacture, will also be covered.

Manufacturability of future battery technologies is addressed in this roadmap from the

perspective of Industry 4.0 and digitalization. The power of modelling and of AI will be

Figure 5. Sensoring and self-healing functionalities in

a battery cell.

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exploited to deliver digital twins both for innovative, breakthrough cell geometries, avoiding

or substantially minimizing classical trial and error approaches, and manufacturing

methodologies, through fully digital manufacturing analogues that allow understanding and

optimization of parameters and their influence in the final product, prior to its physical

implementation.

Eco-design criteria, including design to allow easy disassembly for recycling of parts or

materials, will be facilitated both at cell design and at the manufacturing level.

Integration of cell design and manufacturing design loops will serve to reduce the number of

experiment and will provide insight information to the designers such as the prediction of the

cell performance and durability. The current development procedure is expected to be

improved by the digital twin approach as a result of iterative work on modelling, prototyping

and characterisation. The digital twin concept will be a basis for in-depth study of cell design

improvement and new manufacturability approaches.

The complexity of materials rises with each new generation, and the change of cell

technology affects cell design and manufacturing. The modelling and simulation work of the

BATTERY 2030+ initiative will establish synergies with the laboratory testing experiments:

the researchers will be able to better set out the experimental matrices thanks to a greater

understanding of material properties and cell behaviour, thus reducing the number of trial

error experiments, and substituting them with more guided experimental work based on

predictions of the robust simulation tools.

Many other factors of project planning influences the timing and resources allocated to this

development process, but one thing is clear: the availability of a quick prototyping procedure

supported by effective simulation software revolving around the digital twin concept would

remarkably increase the competitiveness of new and established European cell manufacturers.

The implementation of these techniques and methodologies calls for a sequential, step-by-step

development. Central to this process is the development of physical modelling tools as a

source of information – data – as feedstock to the AI tools.

In the short term This would be done starting from state-of-the art information, and focus

will be the battery cell design methodology. This would include improvement of simulation

tools – multiphysics models- with the goals of reducing the computational burden and

implementation of current AI techniques through deep learning and machine learning methods

for cell design..

In medium term Input is expected to come from BIG, MAP, sensing, self-healing, recycling

and other innovation areas that would be integrated into the process. Also, the methodology

will be adapted to manufacturability of new battery technologies, with the launch and

implementation of the AI driven methodology to manufacturing after the developments made

at cell level design: Modelling -> AI -> Manufacturing including new techniques, as well as

the creation of digital twin of a cell manufacturing process.

In the long term Full maturity of the methodology is expected, closing the loop by means of

integration of the cell design and manufacturing design sub-loops as a fully autonomous

system with interface from BIG-MAP. Parts of this methodology can be progressively made

available to the industry before the full package is made available as a commodity to a new

state of the art.

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Chapter 5. Recyclability

The development of battery dismantling and recycling technologies with high efficiencies

well beyond the EU Battery Directives targets of 50 % for most battery technologies is

essential to ensure the long-term sustainability of the battery economy by 2030. This calls for

new, innovative, simple and low-cost processes targeting a very high recycling rate, low

carbon footprint and economic viability that will ensure a rather direct recovery of the active

materials and single, instead of multistep, approaches. Furthermore, the new materials,

interfaces/interphases and cell architectures developed in BATTERY 2030+, call for new

recycling concepts, such as reconditioning or reusing electrodes and vice versa. To pave the

way for such a shift, there will be a direct coupling to material suppliers, cell and battery

manufacturers, to integrate the constraints of recycling in the new suggested battery designs

and manufacturing processes: (1) design-for-sustainability (including eco-design and

economic and social aspects – considering the whole life cycle), (2) design-for-dismantling

and (3) design-for-recycling approaches. In such a way, the BATTERY 2030+ roadmap will

promote a circular economy with reduced waste, lower use of energy and more intelligent use

of strategic resources.

Implementation of designs for sustainability and more specifically designs for recycling is to

be integrated in the algorithms for automated materials discovery (the input parameter can be

a criticality of the raw materials, their toxicity, reduction of the number of different elements

and other socio-economic aspects).

It is the ambition of BATTERY 2030+ to trend to a new recycling model based on: data

collection and analysis, automated pack disassembly to cell level, wherever possible

investigating re-use and re-purposing, automated cell disassembly to maximally

individualized components, and development of selective powder recovery technologies and

for reconditioning them to battery grade active materials that as such are re-useable in

batteries for automotive/stationary applications.

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Figure 6. Future recycling process.

Concluding over a time frame of ten years, a circular model will be developed, incorporating

specific R&I actions, such as preparing a battery design for maximum longevity, considering

re-calibration, refurbishing and the suitability for second life applications and multiple usages.

Integrated sensing and possibly self-healing concepts can be used to identify damaged/aged

components and prepare for their reuse. A suggestions is depicted in Figure 6. It will also

include the development of concepts for the traceability especially of critical raw material

(CRM) throughout the entire cell life, as well as automated cell sorting and evaluation and

development of efficient, single step, cheap and sustainable processes to recover valuable and

critical materials. Artificial intelligence and sorting interfaces/interphases and equipment will

be required to be applied in selective recycling processes, but also versatile processes

applicable to any battery technology will be looked for.

Processes to recover “active” materials will need development, including “reconditioning”

and “requalification” for a close loop usage. But this should not be the only focus, as the life

duration of the batteries may render the technology obsolete at the time of the recycling.

Therefore, a life cycle sustainability analysis is recommended to assess and identify the more

valuable solutions for recycling both from economic and environmental point of views.

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The new process for recyclability will be the basis of a series of R&I actions (see Figure 7)

with the main purpose to have Direct Recycling implemented in the long term:

In the short term: Start building the integration of design for sustainability and dismantling,

develop a system for data collection and analysis, develop technologies for battery

packs/modules sorting and re-use/re-purposing and start the development of automated

disassembly to battery cells.

In the medium term: Automated cell disassembly into individual components will be

developed as well as sorting and recovery technologies for powders and components and their

reconditioning to new active battery grade materials advanced.

In the long term: A full system for Direct Recycling will be developed and qualified.

Figure 7. The 10 year roadmap for recyclability within BATTERY 2030+.

Would the material/components not be suitable to be reconditioned to battery grade because

of e.g. structural or purity constraints, a fall-back alternative in the last stage of the new

process could be to convert them to precursors with an eventual change of composition ratio’s

anticipating future chemistry changes and new generation materials.

Recycling of lithium-ion batteries from vehicles is still a developing business with large

volumes expected to be recycled already onwards 2030. Since recycling is a cost-intensive

industry, due to the current low volumes, recyclers struggle to find the best balance between

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economics and meeting the recovery targets, resulting in the industry not yet focussing

enough on high efficiency and low emissions. BATTERY 2030+ Recycling Program will

prepare the future to be ready to treat the expected large volumes in a responsible way.

Chapter 6. Other areas to address in a large-scale research program

The purpose of BATTERY 2030+ is to be a long-term (at least 10 years) and large-scale

initiative. This means that the areas of research described in the previous chapters should be

seen as the starting points of a full-scale program. The vision is to enlarge and broaden the

R&I portfolio to become a motor for inventing the batteries of the future. Therefore the

BATTERY 2030+ roadmap is open for new research areas in addition to the ones described in

the previous chapters. The European scientific and industry communities are welcome to

contribute with new ideas, which should be as far as possible compatible with the BATTERY

2030+ “chemistry-neutral” approach (see the Introduction of this document). The full

roadmap presented early next year will contain examples of new research areas, based on the

discussions that will take place during the workshop to be held on the 20th

of November in

Brussels.